Industries & Sectors – Computing for Research · Cloud GPU from Germany

Science: Computing power for Research

Simulations, data analysis and research data management – with GPU and CPU power booked per project instead of spending years in a funding application. Flexible, cost-efficient and always up to date.

ccloud³ Console · research project
eu-de · Hallstadt Data Centre
GPU
A100 80 GB
Billing
by the hour
Budget
quota
GPU Training job · dedicated, not shared
● Running
NB Jupyter · interactive analysis
● Connected
S3 Research data · versioned in S3
● Archived
REP cReports · usage per project
● Exported
Parameter study finishedcluster · scaled to zero
  • Maximum computing capacity – GPU power without your own infrastructure.
  • Latest GPU technology – dedicated NVIDIA cards, regularly refreshed.
  • Flexibly scalable – increase or reduce performance as needed.
  • Transparent costs – no hidden costs, billed by the hour, with no minimum contract term.
Why centron

Infrastructure for projects rather than applications

Research is project-based – computing power should be too.

GPU for computations

Simulations and machine learning analyses run on dedicated GPUs from €92.59 per month (billed by the hour) – booked for the duration of the project and charged to third-party funding.

FAIR research data

S3 Object Storage makes datasets available in versioned and citable formats – from raw data to publication supplements.

Clusters as required

Scaling batch jobs in Kubernetes – large parameter studies result in a large number of nodes, after which the cluster shrinks to zero.

Data Protection for Studies

Personal research data remains in German, C5-attested data centres – compliant with ethics committee requirements.

Use cases

Accelerated workloads with NVIDIA GPUs in the cloud

Especially in data-intensive disciplines such as genomics, climate research or materials science, conventional systems are quickly overwhelmed – cloud GPUs process these workloads at scale.

Training

For faster processing and optimisation of large datasets – train models on dedicated GPUs without waiting for shared clusters.

Fine-tuning

For higher accuracy through targeted optimisation – existing models are adapted to your research question, billed by the hour only for the computing time.

Inference

For the efficient use of trained models for more precise analyses – from screening large datasets to evaluating ongoing measurement series.

Simulation, rendering & big data

Compute-intensive workloads such as deep learning, 3D rendering, scientific simulations and big data analyses run efficiently – with maximum flexibility and without high investment costs.

From a laptop to a cluster

Reproducible research on reproducible infrastructure

Analyses are carried out in Jupyter on a VM, scaled as container jobs in Kubernetes, and the results – along with the environment – are archived in S3; every publication remains traceable. Project-specific billing provides quotas and cReports for the management of third-party funding.

projektgenauhourly-based · ideal for externally funded projects
Calculate costs using the price calculator
  • Interactive – Jupyter & RStudio on VMs
  • Batch processing – Kubernetes for parametric studies
  • Data Archive – S3 versioned and citable
  • Suitable for third-party funding – Costs per project
Recommended modules

The right centron products

Customers typically implement this use case using these building blocks – which can be combined and expanded at any time.

Cloud GPU
From
€92.59 / month
NVIDIA performance
  • RTX A4000 from €92.59 per month
  • Dedicated, not shared
  • No minimum term
Kubernetes
From
€29.99 / month
Container orchestration
  • AutoScaler included
  • Traffic at a fixed price
  • CI/CD-ready
S3 Object Storage
From
€5.00 / month
Scalable storage
  • S3-compatible API
  • Free traffic
  • Unlimited scalability
In a nutshell

Which cloud infrastructure is best suited to the scientific community?

Cloud for science: computing clusters, GPU nodes and storage for research projects – flexible, eligible for funding, based in Germany. The core component is Cloud GPU, starting from €92.59 per month – billed by the hour, with no minimum contract term. This is supplemented, as required, by Kubernetes and S3 Object Storage. The service is hosted in compliance with the GDPR in centron’s own certified to ISO 27001 on the basis of IT-Grundschutz data centres, which hold a BSI C5:2020 Type 1 attestation. New accounts receive a €200 starting credit.

Packages and prices
Building blockPrice
Cloud GPUfrom €92.59 per month
Kubernetesfrom €29.99 per month
S3 Object Storagefrom €5.00 per month
Industries & sectors FAQ

Frequently asked questions

Why are cloud GPUs so useful for scientific research?

Scientific disciplines such as genomics, climate research or AI-assisted analyses generate ever larger datasets that demand enormous computing power. Local hardware solutions are often expensive, maintenance-heavy and quickly outdated. Cloud GPUs offer maximum performance, scalability and always up-to-date technology to run demanding computations efficiently.

How can a cloud GPU be described?

A cloud GPU is a virtualised graphics processing unit provided via the cloud, enabling demanding computations without operating physical hardware. Scalable GPU resources process compute-intensive workloads such as deep learning, 3D rendering, scientific simulations and big data analyses efficiently – with maximum flexibility and without high investment costs. At centron the GPUs are dedicated, not shared.

What advantages do cloud GPUs offer over local GPU servers?

Using cloud GPUs eliminates high acquisition costs, reduces maintenance effort and ensures constant access to modern hardware: maximum computing capacity without your own infrastructure, regular updates to the latest GPU technology, and flexible scalability to increase or reduce performance as needed.

Does centron also offer servers for research projects?

Yes. Alongside cloud GPUs there are ccloud³ Virtual Machines with fast provisioning and dynamic scaling, as well as Managed Servers fully looked after by centron – leaving internal IT free for the research itself.

Is this cost-recovery model suitable for externally funded projects?

Excellent: Resources are billed by the hour for each project, quotas strictly limit budgets, and cReports provides the expenditure reports detailing how funds have been used – without any long-term commitment extending beyond the project duration.

How much does it cost to get started?

The starting prices are deliberately low: Cloud GPU from €92.59 per month, Kubernetes from €29.99 per month and S3 Object Storage from €5.00 per month – with no hidden costs, you pay only for the capacity you use. New accounts receive €200 in starting credit for 60 days; you can calculate your specific configuration transparently in the price calculator.

Can this be implemented in a way that complies with the GDPR?

Yes. In research, data relating to participants and studies often falls within the special categories set out in Article 9 of the GDPR; in addition, there are requirements from the ethics committee and funding bodies regarding the location of data storage. With centron, this data remains in Germany, under a data processing agreement in accordance with Article 28 of the GDPR. The data centres are certified to ISO 27001 on the basis of IT-Grundschutz; for ccloud³ / Managed Cloud there is an unrestricted BSI C5:2020 Type 1 attestation. Details can be found in the Trust Centre.

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